Connectivity forests for homological analysis of digital volumes

In this paper, we provide a graph-based representation of the homology (information related to the different “holes” the object has) of a binary digital volume. We analyze the digital volume AT-model representation [8] from this point of view and the cellular version of the AT-model [5] is precisely...

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Detalhes bibliográficos
Autor: Real Jurado, Pedro
Formato: capítulo de livro
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2009
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/31743
Acesso em linha:http://hdl.handle.net/11441/31743
https://doi.org/10.1007/978-3-642-02478-8_52
Access Level:acceso abierto
Palavra-chave:Computational Biology
Bioinformatics
Pattern Recognition
Artificial Intelligence (incl. Robotics)
Data Mining and Knowledge Discovery
Models and Principles
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spelling Connectivity forests for homological analysis of digital volumesReal Jurado, PedroComputational BiologyBioinformaticsPattern RecognitionArtificial Intelligence (incl. Robotics)Data Mining and Knowledge DiscoveryModels and PrinciplesBioinformaticsIn this paper, we provide a graph-based representation of the homology (information related to the different “holes” the object has) of a binary digital volume. We analyze the digital volume AT-model representation [8] from this point of view and the cellular version of the AT-model [5] is precisely described here as three forests (connectivity forests), from which, for instance, we can straightforwardly determine representative curves of “tunnels” and “holes”, classify cycles in the complex, computing higher (co)homology operations,... Depending of the order in which we gradually construct these trees, tools so important in Computer Vision and Digital Image Processing as Reeb graphs and topological skeletons appear as results of pruning these graphs.Matemática Aplicada I2009info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/31743https://doi.org/10.1007/978-3-642-02478-8_52reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésBio-Inspired Systems: Computational and Ambient Intelligence, Lecture Notes in Computer Science, Vol. 5517 p. 415-423info:eu-repo/semantics/openAccessoai:idus.us.es:11441/317432026-06-17T12:51:07Z
dc.title.none.fl_str_mv Connectivity forests for homological analysis of digital volumes
title Connectivity forests for homological analysis of digital volumes
spellingShingle Connectivity forests for homological analysis of digital volumes
Real Jurado, Pedro
Computational Biology
Bioinformatics
Pattern Recognition
Artificial Intelligence (incl. Robotics)
Data Mining and Knowledge Discovery
Models and Principles
Bioinformatics
title_short Connectivity forests for homological analysis of digital volumes
title_full Connectivity forests for homological analysis of digital volumes
title_fullStr Connectivity forests for homological analysis of digital volumes
title_full_unstemmed Connectivity forests for homological analysis of digital volumes
title_sort Connectivity forests for homological analysis of digital volumes
dc.creator.none.fl_str_mv Real Jurado, Pedro
author Real Jurado, Pedro
author_facet Real Jurado, Pedro
author_role author
dc.contributor.none.fl_str_mv Matemática Aplicada I
dc.subject.none.fl_str_mv Computational Biology
Bioinformatics
Pattern Recognition
Artificial Intelligence (incl. Robotics)
Data Mining and Knowledge Discovery
Models and Principles
Bioinformatics
topic Computational Biology
Bioinformatics
Pattern Recognition
Artificial Intelligence (incl. Robotics)
Data Mining and Knowledge Discovery
Models and Principles
Bioinformatics
description In this paper, we provide a graph-based representation of the homology (information related to the different “holes” the object has) of a binary digital volume. We analyze the digital volume AT-model representation [8] from this point of view and the cellular version of the AT-model [5] is precisely described here as three forests (connectivity forests), from which, for instance, we can straightforwardly determine representative curves of “tunnels” and “holes”, classify cycles in the complex, computing higher (co)homology operations,... Depending of the order in which we gradually construct these trees, tools so important in Computer Vision and Digital Image Processing as Reeb graphs and topological skeletons appear as results of pruning these graphs.
publishDate 2009
dc.date.none.fl_str_mv 2009
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/submittedVersion
format bookPart
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/31743
https://doi.org/10.1007/978-3-642-02478-8_52
url http://hdl.handle.net/11441/31743
https://doi.org/10.1007/978-3-642-02478-8_52
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Bio-Inspired Systems: Computational and Ambient Intelligence, Lecture Notes in Computer Science, Vol. 5517 p. 415-423
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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